Many image databases have been proposed to search a large number of image data for a desired image. Most of these databases are roughly categorized into two methods:                a method of relating non-image information such as a keyword, photographing date, and the like to images, and making a search based on such information; and        a method of making a search based on feature amounts (luminance, color difference information, image frequency, histogram, and the like) of images themselves.        
In either methods, query information and image data are normally managed separately. For example, query data are managed using a single file or relational database and actually undergo a search. File names of image data that match the query condition are obtained from the search results, and image data are accessed and displayed based on these file names. The reason why such method is used is that image data normally has a large size and it is efficient to manage such image data independently from query data.
Individual image data are managed in a file system, and two management methods are available. The first method manages all image data using a single directory. The second method divides image data into some groups each including a plurality of images, and classifies and manages image data in directories in units of groups.
For example, image data may be classified in directories based on their contents such as “animals”, “flowers”, and the like.
However, in both the first and second methods, when a plurality of images obtained as a result of a search using a query key or the like are to be displayed at the same time, if the number of images is large, image access requires a very long period of time.
In the first method, it is easy to manage images. However, if the number of images is too large, a very long time is required to acquire only directory information. In the second method, correspondence between image files and directories must be correctly maintained, and management such as file movement or the like is troublesome.
The present inventors previously proposed a technique for storing a plurality of images and their feature amount data in a single file in, e.g., U.S. application Ser. No. 09/384,965. This technique can search for an image with highest similarity on the basis of the feature amounts of an image as query keys and a plurality of images as test images, and can display an image as a search result at high speed.
However, in the above proposal, the data size of an image file becomes very large with increasing number of images included in the image file. To solve this problem, individual image data included in the image file must have a relatively low resolution. As a result, even when such image is output to a high-resolution printer, it becomes difficult to obtain a high-quality image output.